Diffusion tensor imaging MR neurography for the detection of polyneuropathy in Type 1 diabetes
Purpose: To evaluate if diffusion tensor imaging MR neurography (DTI-MRN) can detect lesions of peripheral nerves in patients with type 1 diabetes. Materials and Methods: Eleven type 1 diabetic patients with polyneuropathy (DPN), 10 type 1 diabetic patients without polyneuropathy (nDPN), and 10 heal...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2017
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| In: |
Journal of magnetic resonance imaging
Year: 2017, Jahrgang: 45, Heft: 4, Pages: 1125-1134 |
| ISSN: | 1522-2586 |
| DOI: | 10.1002/jmri.25415 |
| Online-Zugang: | Verlag, Pay-per-use, Volltext: http://dx.doi.org/10.1002/jmri.25415 Verlag, Pay-per-use, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.25415 
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| Verfasserangaben: | Michael Vaeggemose, Mirko Pham, Steffen Ringgaard, Hatice Tankisi, Niels Ejskjaer, Sabine Heiland, Per L. Poulsen, and Henning Andersen |
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| 245 | 1 | 0 | |a Diffusion tensor imaging MR neurography for the detection of polyneuropathy in Type 1 diabetes |c Michael Vaeggemose, Mirko Pham, Steffen Ringgaard, Hatice Tankisi, Niels Ejskjaer, Sabine Heiland, Per L. Poulsen, and Henning Andersen |
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| 520 | |a Purpose: To evaluate if diffusion tensor imaging MR neurography (DTI-MRN) can detect lesions of peripheral nerves in patients with type 1 diabetes. Materials and Methods: Eleven type 1 diabetic patients with polyneuropathy (DPN), 10 type 1 diabetic patients without polyneuropathy (nDPN), and 10 healthy controls (HC) were investigated with a 3T MRI scanner. Clinical examinations, nerve-conduction studies, and vibratory-perception thresholds determined the presence of DPN. DTI-MRN (voxel size: 1.4 × 1.4 × 3 mm3; b-values: 0, 800 s/mm2) covered proximal (sciatic nerve) and distal regions of the lower extremity (tibial nerve). Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were calculated and compared to T2-relaxometry and proton-spin density obtained from a multiecho turbo spin echo (TSE) sequence. Furthermore, we evaluated DTI reproducibility, repeatability, and diagnostic accuracy. Results: DTI-MRN could accurately discriminate between DPN, nDPN, and HC. The proximal FA was lowest in DPN (DPN 0.37 ± 0.06; nDPN 0.47 ± 0.03; HC 0.49 ± 0.06; P < 0.01). In addition, distal FA was lowest in DPN (DPN 0.31 ± 0.05; nDPN 0.41 ± 0.07; HC 0.43 ± 0.08; P < 0.01). Likewise, proximal ADC was highest in DPN (DPN 1.69 ± 0.25 × 10−3mm2/s; nDPN 1.50 ± 0.06 × 10−3mm2/s; HC 1.42 ± 0.12 × 10−3mm2/s; P < 0.01) as was distal ADC (DPN 1.87 ± 0.45 × 10−3mm2/s; nDPN 1.59 ± 0.19 × 10−3mm2/s; HC 1.57 ± 0.26 × 10−3mm2/s; P = 0.09). The combined interclass-correlation (ICC) coefficient of DTI reproducibility and repeatability was high in the sciatic nerve (ICC: FA = 0.86; ADC = 0.85) and the tibial nerve (ICC: FA = 0.78; ADC = 0.66). T2-relaxometry and proton-spin-density did not enable detection of neuropathy. Conclusion: DTI-MRN accurately detects DPN by lower nerve FA and higher ADC. These alterations are likely to reflect proximal and distal nerve fiber pathology. Level of Evidence: 1 | ||
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| 650 | 4 | |a neuropathy | |
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| 650 | 4 | |a reproducibility | |
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